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作 者:姚敏欣
机构地区:[1]广西大学经济学院,南宁530004
出 处:《中国医疗保险》2024年第6期105-118,共14页China Health Insurance
基 金:国家自然科学基金“基于空间动态时变关联视角的国际股指波动风险测度与控制策略研究”(72161001)。
摘 要:目的:针对日益受到关注的带病体商业健康保险投保问题,本研究以心血管疾病为例分析患者住院费用预测与影响因素,为带病体商业健康保险策略的完善提供参考。方法:综合评估六种主流机器学习算法,并选定模型的参数进行贝叶斯优化,以提高心血管疾病患者住院费用预测准确性。研究深入分析了客户特征、疾病特性和地域属性等关键因素对住院费用的影响,以期揭示这些因素与费用之间的相关性。结果:梯度提升决策树-高斯过程(GBDF-GP)模型在预测心血管疾病患者住院费用方面表现最佳。分析显示,客户属性是费用的主要影响因素,疾病属性次之,地域因素仅对91至100岁患者群体产生显著影响。建议保险公司应用GBDF-GP模型来拓宽承保范围,并对不同客户群体实施精细化的风险评估。此外,研究结果为推动保险产品的创新和促进保险市场的多元化提供了实证基础。Objective:In view of the increasingly concerned issue of commercial health insurance participation of sick persons,this study took cardiovascular diseases as an example to analyze the prediction and influencing factors of hospitalization costs,so as to provide a reference for the improvement of commercial health insurance strategies of sick persons.Methods:Six mainstream machine learning algorithms were evaluated comprehensively,and the parameters of the model were selected for Bayesian optimization to improve the accuracy of hospitalization cost prediction for patients with cardiovascular diseases.The study deeply analyzed the influence of key factors such as client characteristics,disease characteristics and geographical attributes on hospitalization costs in order to reveal the correlation between these factors and costs.Results:The gradient ascending decision tree-Gaussian process(GBDF-GP)model showed the best performance in predicting hospital expenses of patients with cardiovascular disease.The analysis shows that customer attributes are the main influencing factor of cost,followed by disease attributes,and geographical factors only have a significant impact on patients aged from 91 to 100.It is recommended that insurance companies apply GBDF-GP model to broaden coverage and carry out refined risk assessment for different customer groups.In addition,the research results provide an empirical basis for promoting the innovation of insurance products and promoting the diversification of insurance market.
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